Files
AutoGPT/autogpt_platform/analytics/queries/retention_login_daily.sql
Zamil Majdy a8259ca935 feat(analytics): read-only SQL views layer with analytics schema (#12367)
### Changes 🏗️

Adds `autogpt_platform/analytics/` — 14 SQL view definitions that expose
production data safely through a locked-down `analytics` schema.

**Security model:**
- Views use `security_invoker = false` (PostgreSQL 15+), so they execute
as their owner (`postgres`), not the caller
- `analytics_readonly` role only has access to `analytics.*` — cannot
touch `platform` or `auth` tables directly

**Files:**
- `backend/generate_views.py` — does everything; auto-reads credentials
from `backend/.env`
- `analytics/queries/*.sql` — 14 documented view definitions (auth, user
activity, executions, onboarding funnel, cohort retention)

---

### Running locally (dev)

```bash
cd autogpt_platform/backend

# First time only — creates analytics schema, role, grants
poetry run analytics-setup

# Create / refresh views (auto-reads backend/.env)
poetry run analytics-views
```

### Running in production (Supabase)

```bash
cd autogpt_platform/backend

# Step 1 — first time only (run in Supabase SQL Editor as postgres superuser)
poetry run analytics-setup --dry-run
# Paste the output into Supabase SQL Editor and run

# Step 2 — apply views (use direct connection host, not pooler)
poetry run analytics-views --db-url "postgresql://postgres:PASSWORD@db.<ref>.supabase.co:5432/postgres"

# Step 3 — set password for analytics_readonly so external tools can connect
# Run in Supabase SQL Editor:
# ALTER ROLE analytics_readonly WITH PASSWORD 'your-password';
```

---

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Setup + views applied cleanly on local Postgres 15
- [x] `analytics_readonly` can `SELECT` from all 14 `analytics.*` views
- [x] `analytics_readonly` gets `permission denied` on `platform.*` and
`auth.*` directly

---------

Co-authored-by: Otto (AGPT) <otto@agpt.co>
2026-03-13 12:04:42 +00:00

95 lines
4.7 KiB
SQL

-- =============================================================
-- View: analytics.retention_login_daily
-- Looker source alias: ds112 | Charts: 1
-- =============================================================
-- DESCRIPTION
-- Daily cohort retention based on login sessions.
-- Same logic as retention_login_weekly but at day granularity,
-- showing up to day 30 for cohorts from the last 90 days.
-- Useful for analysing early activation (days 1-7) in detail.
--
-- SOURCE TABLES
-- auth.sessions — Login session records
--
-- OUTPUT COLUMNS (same pattern as retention_login_weekly)
-- cohort_day_start DATE First day the cohort logged in
-- cohort_label TEXT Date string (e.g. '2025-03-01')
-- cohort_label_n TEXT Date + cohort size (e.g. '2025-03-01 (n=12)')
-- user_lifetime_day INT Days since first login (0 = signup day)
-- cohort_users BIGINT Total users in cohort
-- active_users_bounded BIGINT Users active on exactly day k
-- retained_users_unbounded BIGINT Users active any time on/after day k
-- retention_rate_bounded FLOAT bounded / cohort_users
-- retention_rate_unbounded FLOAT unbounded / cohort_users
-- cohort_users_d0 BIGINT cohort_users only at day 0, else 0 (safe to SUM)
--
-- EXAMPLE QUERIES
-- -- Day-1 retention rate (came back next day)
-- SELECT cohort_label, retention_rate_bounded AS d1_retention
-- FROM analytics.retention_login_daily
-- WHERE user_lifetime_day = 1 ORDER BY cohort_day_start;
--
-- -- Average retention curve across all cohorts
-- SELECT user_lifetime_day,
-- SUM(active_users_bounded)::float / NULLIF(SUM(cohort_users_d0), 0) AS avg_retention
-- FROM analytics.retention_login_daily
-- GROUP BY 1 ORDER BY 1;
-- =============================================================
WITH params AS (SELECT 30::int AS max_days, (CURRENT_DATE - INTERVAL '90 days')::date AS cohort_start),
events AS (
SELECT s.user_id::text AS user_id, s.created_at::timestamptz AS created_at,
DATE_TRUNC('day', s.created_at)::date AS day_start
FROM auth.sessions s WHERE s.user_id IS NOT NULL
),
first_login AS (
SELECT user_id, MIN(created_at) AS first_login_time,
DATE_TRUNC('day', MIN(created_at))::date AS cohort_day_start
FROM events GROUP BY 1
HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
),
activity_days AS (SELECT DISTINCT user_id, day_start FROM events),
user_day_age AS (
SELECT ad.user_id, fl.cohort_day_start,
(ad.day_start - DATE_TRUNC('day', fl.first_login_time)::date)::int AS user_lifetime_day
FROM activity_days ad JOIN first_login fl USING (user_id)
WHERE ad.day_start >= DATE_TRUNC('day', fl.first_login_time)::date
),
bounded_counts AS (
SELECT cohort_day_start, user_lifetime_day, COUNT(DISTINCT user_id) AS active_users_bounded
FROM user_day_age WHERE user_lifetime_day >= 0 GROUP BY 1,2
),
last_active AS (
SELECT cohort_day_start, user_id, MAX(user_lifetime_day) AS last_active_day FROM user_day_age GROUP BY 1,2
),
unbounded_counts AS (
SELECT la.cohort_day_start, gs AS user_lifetime_day, COUNT(*) AS retained_users_unbounded
FROM last_active la
CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_day,(SELECT max_days FROM params))) gs
GROUP BY 1,2
),
cohort_sizes AS (SELECT cohort_day_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_login GROUP BY 1),
cohort_caps AS (
SELECT cs.cohort_day_start, cs.cohort_users,
LEAST((SELECT max_days FROM params), GREATEST(0,(CURRENT_DATE-cs.cohort_day_start)::int)) AS cap_days
FROM cohort_sizes cs
),
grid AS (
SELECT cc.cohort_day_start, gs AS user_lifetime_day, cc.cohort_users
FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_days) gs
)
SELECT
g.cohort_day_start,
TO_CHAR(g.cohort_day_start,'YYYY-MM-DD') AS cohort_label,
TO_CHAR(g.cohort_day_start,'YYYY-MM-DD')||' (n='||g.cohort_users||')' AS cohort_label_n,
g.user_lifetime_day, g.cohort_users,
COALESCE(b.active_users_bounded,0) AS active_users_bounded,
COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END AS retention_rate_bounded,
CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
CASE WHEN g.user_lifetime_day=0 THEN g.cohort_users ELSE 0 END AS cohort_users_d0
FROM grid g
LEFT JOIN bounded_counts b ON b.cohort_day_start=g.cohort_day_start AND b.user_lifetime_day=g.user_lifetime_day
LEFT JOIN unbounded_counts u ON u.cohort_day_start=g.cohort_day_start AND u.user_lifetime_day=g.user_lifetime_day
ORDER BY g.cohort_day_start, g.user_lifetime_day;